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Machine Unlearning of Federated Clusters by Pan, Chao; Sima, Jin; Prakash, Saurav; Rana, Vishal; Milenkovic, Olgica is a scholarly article available to read on EtoBox.

What is Machine Unlearning of Federated Clusters about?

Federated clustering (FC) is an unsupervised learning problem that arises in a number of practical applications, including personalized recommender and healthcare systems. With the adoption of recent laws ensuring the "right to be forgotten", the problem of machine unlearning for FC methods has become of significant importance. We introduce, for the first time, the problem of machine unlearning for FC, and propose an efficient unlearning mechanism for a customized secure FC framework. Our FC framework utilizes special initialization procedures that we show are well-suited for unlearning. To protect client data privacy, we develop the secure compressed multiset aggregation (SCMA) framework that addresses sparse secure federated learning (FL) problems encountered during clustering as well as more general problems. To simultaneously facilitate low communication complexity and secret sharing protocols, we integrate Reed-Solomon encoding with special evaluation points into our SCMA pipeline, and prove that the client communication cost is logarithmic in the vector dimension. Additionally, to demonstrate the benefits of our unlearning mechanism over complete retraining, we provide a theo

Author

Pan, Chao; Sima, Jin; Prakash, Saurav; Rana, Vishal; Milenkovic, Olgica

Published

2022

Language

EN